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Implications of macroeconomic conditions on Romanian portfolio credit risk. A cost-sensitive ensemble learning methods comparison

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  • Ana-Maria Sandica
  • Alexandra Fratila (Adam)

Abstract

Credit risk assessment represents a key instrument in the decision-making of the banking and financial institutions. In this article, we present a framework for credit risk strategies to improve portfolio efficiency under a change of macroeconomic regime. The aim is to compare the accuracy of several ensemble methods (AdaBoost, Logit Boost, Gentle Boost and Random Forest) on a default retail Romanian loan portfolio under different risk adversity scenarios, a priori and posteriori the financial distress. Using cost-sensitive ensemble learning models, we concluded that regime-based credit strategy can offer a better alternative in both terms of loss allocated to the strategy as well as defaulters’ classification accuracy.

Suggested Citation

  • Ana-Maria Sandica & Alexandra Fratila (Adam), 2022. "Implications of macroeconomic conditions on Romanian portfolio credit risk. A cost-sensitive ensemble learning methods comparison," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 35(1), pages 3571-3590, December.
    • Handle: RePEc:taf:reroxx:v:35:y:2022:i:1:p:3571-3590
    DOI: 10.1080/1331677X.2021.1997625
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